Soil water storage, rainfall and runoff relationships in a tropical dry forest catchment
Bibliographic record
Abstract
Abstract In forested catchments, the exceedance of rainfall and antecedent water storage thresholds is often required for runoff generation, yet to our knowledge these threshold relationships remain undescribed in tropical dry forest catchments. We, therefore, identified the controls of streamflow activation and the timing and magnitude of runoff in a tropical dry forest catchment near the Pacific coast of central Mexico. During a 52 day transition phase from the dry to wet season, soil water movement was dominated by vertical flow which continued until a threshold soil moisture content of 26% was reached at 100 cm below the surface. This satisfied a 162 mm storage deficit and activated streamflow, likely through lateral subsurface flow pathways. High antecedent soil water conditions were maintained during the wet phase but had a weak influence on stormflow. We identified a threshold value of 289 mm of summed rainfall and antecedent soil water needed to generate >4 mm of stormflow per event. Above this threshold, stormflow response and magnitude was almost entirely governed by rainfall event characteristics and not antecedent soil moisture conditions. Our results show that over the course of the wet season in tropical dry forests the dominant controls on runoff generation changed from antecedent soil water and storage to the depth of rainfall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".